Metaprogramming JavaScript

Nov 21, 2007159 likes43,673 views

JavaScript allows for metaprogramming through manipulating programs as data. It relies on few but powerful constructs including working with functions, arguments, built-in types, and mechanisms like inheritance at runtime. The key building blocks are objects, numbers, strings, booleans, and undefined, with everything else being objects. Functions are objects that can return other functions, taking advantage of closures to refer to outer variables from inner functions. This flexibility allows for patching implementations, self-optimizing code, custom APIs, and domain-specific languages.

Metaprogramming involves writing computer programs that manipulate or generate other programs. It allows for more elegant code, less boilerplate code, and hiding implementation details. The document discusses different ways metaprogramming is implemented in JavaScript, including dynamic features like reflection, getters/setters, proxies, decorators, and generators, as well as code generation approaches. Real-world examples of metaprogramming include frameworks, domain-specific languages, and adding functionality without cluttering main logic.

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Can We Use Rust to Develop Extensions for PostgreSQL? (POSETTE: An Event for ...NTT DATA Technology & Innovation

AI Agents in Logistics and Supply Chain Applications Benefits and ImplementationChristine Shepherd

AI agents are reshaping logistics and supply chain operations by enabling automation, predictive insights, and real-time decision-making across key functions such as demand forecasting, inventory management, procurement, transportation, and warehouse operations. Powered by technologies like machine learning, NLP, computer vision, and robotic process automation, these agents deliver significant benefits including cost reduction, improved efficiency, greater visibility, and enhanced adaptability to market changes. While practical use cases show measurable gains in areas like dynamic routing and real-time inventory tracking, successful implementation requires careful integration with existing systems, quality data, and strategic scaling. Despite challenges such as data integration and change management, AI agents offer a strong competitive edge, with widespread industry adoption expected by 2025.

Agentic AI: Beyond the Buzz- LangGraph Studio V2Shashikant Jagtap

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Oracle Cloud Infrastructure AI FoundationsVICTOR MAESTRE RAMIREZ

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Integration of Utility Data into 3D BIM Models Using a 3D Solids Modeling Wor...Safe Software

Jacobs has developed a 3D utility solids modelling workflow to improve the integration of utility data into 3D Building Information Modeling (BIM) environments. This workflow, a collaborative effort between the New Zealand Geospatial Team and the Australian Data Capture Team, employs FME to convert 2D utility data into detailed 3D representations, supporting enhanced spatial analysis and clash detection. To enable the automation of this process, Jacobs has also developed a survey data standard that standardizes the capture of existing utilities. This standard ensures consistency in data collection, forming the foundation for the subsequent automated validation and modelling steps. The workflow begins with the acquisition of utility survey data, including attributes such as location, depth, diameter, and material of utility assets like pipes and manholes. This data is validated through a custom-built tool that ensures completeness and logical consistency, including checks for proper connectivity between network components. Following validation, the data is processed using an automated modelling tool to generate 3D solids from 2D geometric representations. These solids are then integrated into BIM models to facilitate compatibility with 3D workflows and enable detailed spatial analyses. The workflow contributes to improved spatial understanding by visualizing the relationships between utilities and other infrastructure elements. The automation of validation and modeling processes ensures consistent and accurate outputs, minimizing errors and increasing workflow efficiency. This methodology highlights the application of FME in addressing challenges associated with geospatial data transformation and demonstrates its utility in enhancing data integration within BIM frameworks. By enabling accurate 3D representation of utility networks, the workflow supports improved design collaboration and decision-making in complex infrastructure projects

Scaling GenAI Inference From Prototype to Production: Real-World Lessons in S...Anish Kumar

Presented by: Anish Kumar LinkedIn: https://www.linkedin.com/in/anishkumar/ This lightning talk dives into real-world GenAI projects that scaled from prototype to production using Databricks’ fully managed tools. Facing cost and time constraints, we leveraged four key Databricks features—Workflows, Model Serving, Serverless Compute, and Notebooks—to build an AI inference pipeline processing millions of documents (text and audiobooks). This approach enables rapid experimentation, easy tuning of GenAI prompts and compute settings, seamless data iteration and efficient quality testing—allowing Data Scientists and Engineers to collaborate effectively. Learn how to design modular, parameterized notebooks that run concurrently, manage dependencies and accelerate AI-driven insights. Whether you're optimizing AI inference, automating complex data workflows or architecting next-gen serverless AI systems, this session delivers actionable strategies to maximize performance while keeping costs low.

Floods in Valencia: Two FME-Powered Stories of Data ResilienceSafe Software

In October 2024, the Spanish region of Valencia faced severe flooding that underscored the critical need for accessible and actionable data. This presentation will explore two innovative use cases where FME facilitated data integration and availability during the crisis. The first case demonstrates how FME was used to process and convert satellite imagery and other geospatial data into formats tailored for rapid analysis by emergency teams. The second case delves into making human mobility data—collected from mobile phone signals—accessible as source-destination matrices, offering key insights into population movements during and after the flooding. These stories highlight how FME's powerful capabilities can bridge the gap between raw data and decision-making, fostering resilience and preparedness in the face of natural disasters. Attendees will gain practical insights into how FME can support crisis management and urban planning in a changing climate.

Edge-banding-machines-edgeteq-s-200-en-.pdfAmirStern2

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Oracle Cloud Infrastructure Generative AI ProfessionalVICTOR MAESTRE RAMIREZ

Bridging the divide: A conversation on tariffs today in the book industry - T...BookNet Canada

A collaboration-focused conversation on the recently imposed US and Canadian tariffs where speakers shared insights into the current legislative landscape, ongoing advocacy efforts, and recommended next steps. This event was presented in partnership with the Book Industry Study Group. Link to accompanying resource: https://bnctechforum.ca/sessions/bridging-the-divide-a-conversation-on-tariffs-today-in-the-book-industry/ Presented by BookNet Canada and the Book Industry Study Group on May 29, 2025 with support from the Department of Canadian Heritage.

Crypto Super 500 - 14th Report - June2025.pdfStephen Perrenod

This OrionX's 14th semi-annual report on the state of the cryptocurrency mining market. The report focuses on Proof-of-Work cryptocurrencies since those use substantial supercomputer power to mint new coins and encode transactions on their blockchains. Only two make the cut this time, Bitcoin with $18 billion of annual economic value produced and Dogecoin with $1 billion. Bitcoin has now reached the Zettascale with typical hash rates of 0.9 Zettahashes per second. Bitcoin is powered by the world's largest decentralized supercomputer in a continuous winner take all lottery incentive network.

PyData - Graph Theory for Multi-Agent Integrationbarqawicloud

Graph theory is a well-known concept for algorithms and can be used to orchestrate the building of multi-model pipelines. By translating tasks and dependencies into a Directed Acyclic Graph, we can orchestrate diverse AI models, including NLP, vision, and recommendation capabilities. This tutorial provides a step-by-step approach to designing graph-based AI model pipelines, focusing on clinical use cases from the field.